Data clustering is a powerful technology and can calssify the objects with similar data characteristics into a class,however,the implementation of all clustering algorithms does not produce the same clustering results,moreover,the results of K-means algorithm largely depend on the selection of initial clustering center.This paper proposes a novel strategy about K-means initial clustering center selection,whose algorithm is based on reverse nearest neighbor search and retrieves a given data set whose nearest neighbor is all point in a given inquiry point.The result by using this algorithm to t=calculate initial clustering center reveals that this center is very close to iterative clustering center needed by clustering algorithm.This paper also verifies the application of the proposed algorithm to K-means cluster and uses the experiment through several popular data sets to demonstrate the advantages of this algorithm.